SOTAVerified

Object Recognition

Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.

( Image credit: Tensorflow Object Detection API )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1701–1725 of 2042 papers

TitleStatusHype
Zero-shot object prediction using semantic scene knowledge—0
Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration—0
Modeling the Contribution of Central Versus Peripheral Vision in Scene, Object, and Face Recognition—0
Humans and deep networks largely agree on which kinds of variation make object recognition harder—0
Automatic Graphic Logo Detection via Fast Region-based Convolutional Networks—0
Can Boosting with SVM as Week Learners Help?—0
Invariant feature extraction from event based stimuli—0
Orientation-boosted Voxel Nets for 3D Object Recognition—0
DTM: Deformable Template Matching—0
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from VideosCode0
Edge Detection Based Shape Identification—0
Correlated and Individual Multi-Modal Deep Learning for RGB-D Object Recognition—0
A Survey on Bayesian Deep LearningCode0
Deep Cross Residual Learning for Multitask Visual RecognitionCode0
Building Machines That Learn and Think Like People—0
How to Transfer? Zero-Shot Object Recognition via Hierarchical Transfer of Semantic Attributes—0
A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity—0
Object Recognition Based on Amounts of Unlabeled Data—0
Object Recognition and Identification Using ESM Data—0
Beyond Sharing Weights for Deep Domain Adaptation—0
Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures—0
Diversity in Object Proposals—0
Investigation of event-based memory surfaces for high-speed tracking, unsupervised feature extraction and object recognition—0
A Feature Learning and Object Recognition Framework for Underwater Fish Images—0
Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain Decoding—0
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All datasetsshape biasCIFAR10-DVSN-Caltech 101ObjectNet (All classes)ObjectNet (ImageNet classes)ObjectNet (ImageNet classes, trained on ImageNet)DVS128 GestureMECCANON-CARS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7—Unverified
2Stable Diffusionshape bias92.7—Unverified
3Partishape bias91.7—Unverified
4ViT-22B-384shape bias86.4—Unverified
5ViT-22B-560shape bias83.8—Unverified
6CLIP (ViT-B)shape bias79.9—Unverified
7ViT-22B-224shape bias78—Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5—Unverified
9ResNet-50 (with strong augmentations)shape bias62.2—Unverified
10SWSL (ResNeXt-101)shape bias49.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55—Unverified
2SSNNAccuracy (% )78.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62—Unverified
2SSNNAccuracy (% )79.25—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75—Unverified
2yunTop 5 Accuracy14.75—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24—Unverified
2DYTop 5 Accuracy0.08—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24—Unverified
2AJ2021Top 5 Accuracy27.68—Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96—Unverified